Pre-visualization of the normalized count data before any differential analysis

Table of content

Check samples and genes with normalized counts
1. Check for artefact due to FACS sorting
2. Check for contamination from other cell types that could have escaped the sorting-gating
Filter contamination genes and contaminated samples
Sample clustering
1. With all genes
2. Without X/Y genes
PCA on the normalized counts
1. With all factors
2. SPF vs GF
Gene co-expression analysis

Generated from a Jupyter Notebook - Sources

Load¶

Libraries and functions¶

Warning message in is.na(x[[i]]):
“is.na() applied to non-(list or vector) of type 'environment'”Warning message in rsqlite_fetch(res@ptr, n = n):
“Don't need to call dbFetch() for statements, only for queries”

==========================================================================
*
*  Package WGCNA 1.63 loaded.
*
*    Important note: It appears that your system supports multi-threading,
*    but it is not enabled within WGCNA in R. 
*    To allow multi-threading within WGCNA with all available cores, use 
*
*          allowWGCNAThreads()
*
*    within R. Use disableWGCNAThreads() to disable threading if necessary.
*    Alternatively, set the following environment variable on your system:
*
*          ALLOW_WGCNA_THREADS=<number_of_processors>
*
*    for example 
*
*          ALLOW_WGCNA_THREADS=4
*
*    To set the environment variable in linux bash shell, type 
*
*           export ALLOW_WGCNA_THREADS=4
*
*     before running R. Other operating systems or shells will
*     have a similar command to achieve the same aim.
*
==========================================================================


Allowing multi-threading with up to 4 threads.
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."

Data¶

Warning message:
“Setting row names on a tibble is deprecated.”Warning message:
“Setting row names on a tibble is deprecated.”Warning message:
“Setting row names on a tibble is deprecated.”Warning message:
“Setting row names on a tibble is deprecated.”Warning message:
“Setting row names on a tibble is deprecated.”Warning message:
“Setting row names on a tibble is deprecated.”

Check samples and specific genes¶

Check for artefact due to FACS sorting¶

List of genes to check: Egr1, Jun, Zfp36l1, Malat1, Dusp1, Nr4a1, Fos

Normalized counts

Z-scores

Check for contamination from other cell types that could have escaped the sorting gating¶

Genes in list, but not in the counts

Normalized counts

Z-score

Filter contamination genes and contaminated samples¶

Genes that could be artefact due to FACS sorting or contamination from other cell types and samples that seem contaminated: 'GF_8w_M_2_2', 'SPF_52w_F_1_2', 'SPF_104w_M_3_2'

Filter the normalized counts (value before and )

Filter the samples from annotations and metadata

Sample clustering¶

Warning message:
“Setting row names on a tibble is deprecated.”

With all genes¶

Clustering method: Ward D2

Without X/Y genes¶

Extract genes from chrX / chrX_GL456233_random / chrY

Warning message:
“Too many values at 30003 locations: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...”

Remove genes (number displayed) from the count table

Clustering method: Ward D2

PCA on the normalized counts¶

With all factors¶

Warning message:
“Setting row names on a tibble is deprecated.”

SPF vs GF¶

Male & Young

Saving 7 x 7 in image

Male & Middle-aged

Saving 7 x 7 in image

Male & Old

Saving 7 x 7 in image

Female & Young

Saving 7 x 7 in image

Female & Middle-aged

Saving 7 x 7 in image

Female & Old

Saving 7 x 7 in image

Z-scores (normalized counts) for all genes¶

All genes¶

Column order: microbiota - sex - age¶

Subset of selected genes¶

GF signature genes¶

Column order: microbiota - sex - age

DNA damage repair genes¶

Column order: microbiota - sex - age

Gene co-expression analysis¶

Weighted gene co-expression network analysis using WGCNA package

Extract modules of co-expressed genes¶

Keep only genes that have a count >= 10 in more than 90% of the samples (number removed / kept displayed)

Analysis of scale free topology for multiple soft thresholding powers, with signed hybrid network type

pickSoftThreshold: will use block size 4353.
 pickSoftThreshold: calculating connectivity for given powers...
   ..working on genes 1 through 4353 of 10277
   ..working on genes 4354 through 8706 of 10277
   ..working on genes 8707 through 10277 of 10277
   Power SFT.R.sq  slope truncated.R.sq  mean.k. median.k. max.k.
1      1    0.116  0.852          0.943 1040.000  1.04e+03 1630.0
2      2    0.353 -1.100          0.941  336.000  3.18e+02  821.0
3      3    0.664 -1.650          0.978  136.000  1.17e+02  496.0
4      4    0.769 -1.920          0.983   64.300  4.86e+01  332.0
5      5    0.806 -2.060          0.979   33.700  2.18e+01  238.0
6      6    0.813 -2.140          0.969   19.100  1.04e+01  178.0
7      7    0.817 -2.190          0.965   11.600  5.21e+00  137.0
8      8    0.842 -2.150          0.975    7.330  2.71e+00  108.0
9      9    0.864 -2.100          0.983    4.840  1.47e+00   87.5
10    10    0.876 -2.060          0.986    3.310  8.04e-01   71.6
11    12    0.897 -1.990          0.991    1.670  2.64e-01   49.7
12    14    0.904 -1.930          0.991    0.921  9.27e-02   35.7
13    16    0.919 -1.870          0.994    0.541  3.43e-02   26.4
14    18    0.930 -1.810          0.997    0.335  1.34e-02   19.9
15    20    0.940 -1.760          0.998    0.216  5.39e-03   15.2

Block-wise network construction and module detection

Soft-thresholding power parameter: 6 (R2 > 0.80 and mean connectivity highest as possible (Ref: https://www.degruyter.com/view/j/sagmb.2005.4.issue-1/sagmb.2005.4.1.1128/sagmb.2005.4.1.1128.pdf))
Minimum module size: 65
Height for module merging: 0.35 (below)
Biweight Midcorrelation

 Calculating module eigengenes block-wise from all genes
   Flagging genes and samples with too many missing values...
    ..step 1
 ..Working on block 1 .
    TOM calculation: adjacency..
    ..will use 4 parallel threads.
     Fraction of slow calculations: 0.000000
    ..connectivity..
    ..matrix multiplication (system BLAS)..
    ..normalization..
    ..done.
   ..saving TOM for block 1 into file norm_genes_TOM-block.1.RData
 ....clustering..
 ....detecting modules..
 ....calculating module eigengenes..
 ....checking kME in modules..
     ..removing 45 genes from module 1 because their KME is too low.
     ..removing 27 genes from module 2 because their KME is too low.
     ..removing 19 genes from module 3 because their KME is too low.
     ..removing 7 genes from module 4 because their KME is too low.
     ..removing 1 genes from module 5 because their KME is too low.
     ..removing 2 genes from module 6 because their KME is too low.
     ..removing 4 genes from module 7 because their KME is too low.
     ..removing 4 genes from module 8 because their KME is too low.
     ..removing 1 genes from module 9 because their KME is too low.
     ..removing 1 genes from module 10 because their KME is too low.
     ..removing 1 genes from module 11 because their KME is too low.
     ..removing 1 genes from module 14 because their KME is too low.
     ..removing 1 genes from module 15 because their KME is too low.
     ..removing 1 genes from module 16 because their KME is too low.
     ..removing 2 genes from module 18 because their KME is too low.
     ..removing 1 genes from module 24 because their KME is too low.
 ..merging modules that are too close..
     mergeCloseModules: Merging modules whose distance is less than 0.35
       Calculating new MEs...

Size of the modules (ME0: genes not assigned to a module) and number of genes in modules

 ME0  ME1  ME2  ME3  ME4  ME5  ME6  ME7  ME8  ME9 
2719 1627 1240 1221 1123  925  467  467  371  117

Dendrogram and the module colors underneath the block

Associate module color to genes

Recalculate MEs with color labels

Extract the palette for next plots

Relationship between modules and samples¶

Module-trait correlation analysis between the module eigengene (ME) and the different trait (combination of Microbiota, age and sex)

correlation of Pearson between each pair of variables
Student asymptotic p-values for the correlations

Genes in modules¶

Associate genes to modules

Heatmaps with Z-scores¶

Microbiota / Sex / Age¶

Sex / Microbiota / Age¶

Age / Microbiota / Sex¶

Sinaplots of the Z-scores per groups¶

The mean of the Z-score over the samples in the group is plot for each gene

Microbiota / Age¶

Microbiota / Age / Sex¶

Age / Microbiota / Sex¶

Enrichment analysis in modules¶

Warning message in grep(txdbPattern, installedPackages):
“argument 'pattern' has length > 1 and only the first element will be used”Warning message in pcls(G):
“initial point very close to some inequality constraints”

[1] "ME0"

Warning message in grep(txdbPattern, installedPackages):
“argument 'pattern' has length > 1 and only the first element will be used”Warning message in pcls(G):
“initial point very close to some inequality constraints”Fetching GO annotations...
For 171 genes, we could not find any categories. These genes will be excluded.
To force their use, please run with use_genes_without_cat=TRUE (see documentation).
This was the default behavior for version 1.15.1 and earlier.
Calculating the p-values...
'select()' returned 1:1 mapping between keys and columns

[1] category                 over_represented_pvalue  under_represented_pvalue
[4] numDEInCat               numInCat                 term                    
[7] ontology                
<0 rows> (or 0-length row.names)
[1] "ME1"

Warning message in grep(txdbPattern, installedPackages):
“argument 'pattern' has length > 1 and only the first element will be used”Warning message in pcls(G):
“initial point very close to some inequality constraints”Fetching GO annotations...
For 171 genes, we could not find any categories. These genes will be excluded.
To force their use, please run with use_genes_without_cat=TRUE (see documentation).
This was the default behavior for version 1.15.1 and earlier.
Calculating the p-values...
'select()' returned 1:1 mapping between keys and columns

        category over_represented_pvalue under_represented_pvalue numDEInCat
12376 GO:0051252            4.278302e-06                 0.999997        436
5808  GO:0019219            1.340659e-05                 0.999990        469
      numInCat                                                           term
12376     2015                            regulation of RNA metabolic process
5808      2222 regulation of nucleobase-containing compound metabolic process
      ontology
12376       BP
5808        BP
[1] "ME2"

Warning message in grep(txdbPattern, installedPackages):
“argument 'pattern' has length > 1 and only the first element will be used”Fetching GO annotations...
For 171 genes, we could not find any categories. These genes will be excluded.
To force their use, please run with use_genes_without_cat=TRUE (see documentation).
This was the default behavior for version 1.15.1 and earlier.
Calculating the p-values...
'select()' returned 1:1 mapping between keys and columns

        category over_represented_pvalue under_represented_pvalue numDEInCat
10532 GO:0044712            2.207706e-09                1.0000000         97
10522 GO:0044699            1.999958e-08                1.0000000        825
10548 GO:0044763            9.184695e-08                1.0000000        741
10530 GO:0044710            6.896704e-07                0.9999997        326
4111  GO:0009987            8.402822e-07                1.0000000        997
3796  GO:0009056            1.097148e-06                0.9999996        185
      numInCat                              term ontology
10532      419 single-organism catabolic process       BP
10522     6250           single-organism process       BP
10548     5618  single-organism cellular process       BP
10530     2074 single-organism metabolic process       BP
4111      7767                  cellular process       BP
3796      1105                 catabolic process       BP
[1] "ME3"

Warning message in grep(txdbPattern, installedPackages):
“argument 'pattern' has length > 1 and only the first element will be used”Warning message in pcls(G):
“initial point very close to some inequality constraints”Fetching GO annotations...
For 171 genes, we could not find any categories. These genes will be excluded.
To force their use, please run with use_genes_without_cat=TRUE (see documentation).
This was the default behavior for version 1.15.1 and earlier.
Calculating the p-values...
'select()' returned 1:1 mapping between keys and columns

        category over_represented_pvalue under_represented_pvalue numDEInCat
2888  GO:0006643            3.133964e-07                0.9999999         34
11236 GO:0046467            4.161297e-07                0.9999999         26
9239  GO:0036503            2.630508e-06                0.9999993         25
6569  GO:0030433            2.869772e-06                0.9999993         22
16085 GO:1901137            3.216894e-06                0.9999986         74
10364 GO:0044255            6.599740e-06                0.9999966         94
      numInCat                                                        term
2888       113                            membrane lipid metabolic process
11236       76                         membrane lipid biosynthetic process
9239        78                                                ERAD pathway
6569        64 ER-associated ubiquitin-dependent protein catabolic process
16085      366                carbohydrate derivative biosynthetic process
10364      498                            cellular lipid metabolic process
      ontology
2888        BP
11236       BP
9239        BP
6569        BP
16085       BP
10364       BP
[1] "ME4"

Warning message in grep(txdbPattern, installedPackages):
“argument 'pattern' has length > 1 and only the first element will be used”Warning message in pcls(G):
“initial point very close to some inequality constraints”Fetching GO annotations...
For 171 genes, we could not find any categories. These genes will be excluded.
To force their use, please run with use_genes_without_cat=TRUE (see documentation).
This was the default behavior for version 1.15.1 and earlier.
Calculating the p-values...
'select()' returned 1:1 mapping between keys and columns

Citations¶

To cite WGCNA in publications use:

  Langfelder P and Horvath S, WGCNA: an R package for weighted
  correlation network analysis. BMC Bioinformatics 2008, 9:559
  doi:10.1186/1471-2105-9-559

  Peter Langfelder, Steve Horvath (2012). Fast R Functions for Robust
  Correlations and Hierarchical Clustering. Journal of Statistical
  Software, 46(11), 1-17. URL http://www.jstatsoft.org/v46/i11/.

We have invested a lot of time and effort in creating the package,
please cite it when using it for data analysis.
To see these entries in BibTeX format, use 'print(<citation>,
bibtex=TRUE)', 'toBibtex(.)', or set
'options(citation.bibtex.max=999)'.

To cite sinaplot in publications, please use:

  Sidiropoulos. N, Sohi S.H., Rapin N., Bagger F.O. (2017). An Enhanced
  Chart for Simple and Truthful Representation of Single Observations
  over Multiple Classes, R package version 1.1.0.

  Sidiropoulos. N, Sohi S.H., Rapin N., Bagger F.O., SinaPlot: an
  enhanced chart for simple and truthful representation of single
  observations over multiple classes. bioRxiv doi:
  http://dx.doi.org/10.1101/028191

To see these entries in BibTeX format, use 'print(<citation>,
bibtex=TRUE)', 'toBibtex(.)', or set
'options(citation.bibtex.max=999)'.

@Manual{,
  title = {An Enhanced Chart for Simple and Truthful Representation of
Single Observations over Multiple Classes},
  author = {Sidiropoulos N. and Sohi S.H. and Rapin N. and Bagger F.O.},
  publisher = {manual},
  year = {2017},
  note = {R package version 1.1.0},
  url = {https://cran.r-project.org/package=sinaplot},
}

@TechReport{,
  title = {SinaPlot: an enhanced chart for simple and truthful representation of single observations over multiple classes},
  author = {Nikos Sidiropoulos and Sina Hadi Sohi and Nicolas Rapin and Frederik Otzen Bagger},
  booktitle = {bioRxiv},
  institution = {Cold Spring Harbor Labs Journals},
  year = {2015},
  month = {Oct},
  doi = {10.1101/028191},
  url = {http://biorxiv.org/content/early/2015/10/02/028191.abstract},
}

The methods within the code package can be cited as:

  Young, M.D., Wakefield, M.J., Smyth, G.K., Oshlack, A., Gene ontology
  analysis for RNA-seq: accounting for selection bias, Genome Biology,
  11, 2, Feb 2010, R14

A BibTeX entry for LaTeX users is

  @Article{,
    title = {Gene ontology analysis for RNA-seq: accounting for selection bias},
    author = {Matthew D Young and Matthew J Wakefield and Gordon K Smyth and Alicia Oshlack},
    journal = {Genome Biology},
    volume = {11},
    pages = {R14},
    year = {2010},
  }

This free open-source software implements academic research by the
authors and co-workers. If you use it, please support the project by
citing the appropriate journal articles.

Warning message in citation("org.Mm.eg.db"):
“no date field in DESCRIPTION file of package ‘org.Mm.eg.db’”

To cite package ‘org.Mm.eg.db’ in publications use:

  Marc Carlson (2016). org.Mm.eg.db: Genome wide annotation for Mouse.
  R package version 3.4.0.

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {org.Mm.eg.db: Genome wide annotation for Mouse},
    author = {Marc Carlson},
    year = {2016},
    note = {R package version 3.4.0},
  }

ATTENTION: This citation information has been auto-generated from the
package DESCRIPTION file and may need manual editing, see
‘help("citation")’.

cell	genes
microglia	Tmem119
microglia	Hexb
microglia	P2ry12
microglia	Siglech
microglia	Trem2
microglia	P2ry13
macrophage	Mrc1
macrophage	Cd163
macrophage	Lyve1
macrophage	Siglec1
macrophage	Pf4
monocyte	Ly6c2
monocyte	Ccr2
monocyte	Anxa8
monocyte	Nr4a1
monocyte	Plac8
dc	Flt3
dc	Zbtb46
dc	Itgae
dc	Batf3
dc	Clec9a
granulocyte	Mpo
granulocyte	Ngp
granulocyte	Wfdc17
t	Cd3e
t	Cd3d
t	Nkg7
b	Cd79a
b	Cd19
b	Ebf1
mast	Tpsb2
mast	Cpa3
nk	Gzmb
nk	Eomes

cell	genes
microglia	Scl2a5
granulocyte	Ly6g
t	Trbc1
t	Trbc2
b	Igkc
b	Ighm
mast	Mcpt4
mast	Cma1
nk	NKp46
nk	NK1.1
nk	NKG2D
nk	Tbet

cell	genes
microglia	Tmem119
microglia	Hexb
microglia	P2ry12
microglia	Siglech
microglia	Trem2
microglia	P2ry13
macrophage	Mrc1
macrophage	Cd163
macrophage	Lyve1
macrophage	Siglec1
macrophage	Pf4
monocyte	Ly6c2
monocyte	Ccr2
monocyte	Anxa8
monocyte	Nr4a1
monocyte	Plac8
dc	Flt3
dc	Zbtb46
dc	Itgae
dc	Batf3
dc	Clec9a
granulocyte	Mpo
granulocyte	Ngp
granulocyte	Wfdc17
t	Cd3e
t	Cd3d
t	Nkg7
b	Cd79a
b	Cd19
b	Ebf1
mast	Tpsb2
mast	Cpa3
nk	Gzmb
nk	Eomes

	cell
Tmem119	microglia
Hexb	microglia
P2ry12	microglia
Siglech	microglia
Trem2	microglia
P2ry13	microglia
Mrc1	macrophage
Cd163	macrophage
Lyve1	macrophage
Siglec1	macrophage
Pf4	macrophage
Ly6c2	monocyte
Ccr2	monocyte
Anxa8	monocyte
Nr4a1	monocyte
Plac8	monocyte
Flt3	dc
Zbtb46	dc
Itgae	dc
Batf3	dc
Clec9a	dc
Mpo	granulocyte
Ngp	granulocyte
Wfdc17	granulocyte
Cd3e	t
Cd3d	t
Nkg7	t
Cd79a	b
Cd19	b
Ebf1	b
Tpsb2	mast
Cpa3	mast
Gzmb	nk
Eomes	nk

Power	SFT.R.sq	slope	truncated.R.sq	mean.k.	median.k.	max.k.
1	0.1160946	0.8516606	0.9432081	1043.2319743	1.039858e+03	1631.43013
2	0.3534325	-1.0957144	0.9406479	335.8136383	3.181957e+02	820.65787
3	0.6637248	-1.6460594	0.9780977	136.3881967	1.174060e+02	496.21168
4	0.7685466	-1.9220353	0.9832291	64.3190708	4.862867e+01	332.43621
5	0.8057060	-2.0564238	0.9792358	33.7188688	2.180012e+01	237.66372
6	0.8134695	-2.1404068	0.9685734	19.1364860	1.041504e+01	177.64476
7	0.8167563	-2.1947647	0.9653164	11.5523699	5.214321e+00	137.13604
8	0.8422749	-2.1489288	0.9752509	7.3273056	2.709162e+00	108.47588
9	0.8638155	-2.1041594	0.9828091	4.8391956	1.465842e+00	87.45249
10	0.8760665	-2.0646700	0.9856232	3.3053452	8.042653e-01	71.58565
12	0.8974660	-1.9875884	0.9909974	1.6724937	2.641405e-01	49.69122
14	0.9036729	-1.9326232	0.9905956	0.9211628	9.265626e-02	35.74188
16	0.9189432	-1.8686196	0.9937579	0.5414904	3.432230e-02	26.39679
18	0.9297761	-1.8072410	0.9965048	0.3351597	1.342172e-02	19.89803
20	0.9401800	-1.7582833	0.9982447	0.2163296	5.386947e-03	15.24642

Microbiota	Age	Sex	sample	project
GF	Old	Female	GF_104w_F_1_2	S264
GF	Old	Female	GF_104w_F_2_2	S264
GF	Old	Female	GF_104w_F_3_2	S264
GF	Old	Male	GF_104w_M_1_2	S264
GF	Old	Male	GF_104w_M_2_2	S264
GF	Old	Male	GF_104w_M_3_2	S288
GF	Old	Male	GF_104w_M_4_2	S288
GF	Old	Male	GF_104w_M_5_2	S288
GF	Middle-aged	Female	GF_52w_F_1_2	S178
GF	Middle-aged	Female	GF_52w_F_2_2	S178
GF	Middle-aged	Female	GF_52w_F_3_2	S178
GF	Middle-aged	Female	GF_52w_F_4_2	S178
GF	Middle-aged	Female	GF_52w_F_5_2	S178
GF	Middle-aged	Female	GF_52w_F_6_2	S178
GF	Middle-aged	Male	GF_52w_M_1_2	S148
GF	Middle-aged	Male	GF_52w_M_2_2	S148
GF	Middle-aged	Male	GF_52w_M_3_2	S148
GF	Middle-aged	Male	GF_52w_M_4_2	S148
GF	Young	Female	GF_8w_F_1_2	S225
GF	Young	Female	GF_8w_F_2_2	S225
GF	Young	Female	GF_8w_F_3_2	S225
GF	Young	Female	GF_8w_F_4_2	S225
GF	Young	Female	GF_8w_F_5_2	S225
GF	Young	Male	GF_8w_M_1_2	S148
GF	Young	Male	GF_8w_M_2_2	S148
GF	Young	Male	GF_8w_M_3_2	S148
GF	Young	Male	GF_8w_M_4_2	S148
SPF	Old	Female	SPF_104w_F_1_2	S174
SPF	Old	Female	SPF_104w_F_2_2	S174
SPF	Old	Female	SPF_104w_F_3_2	S174
⋮	⋮	⋮	⋮	⋮
SPF	Old	Male	SPF_104w_M_10_2	S198
SPF	Old	Male	SPF_104w_M_11_2	S198
SPF	Old	Male	SPF_104w_M_12_2	S198
SPF	Old	Male	SPF_104w_M_13_2	S198
SPF	Old	Male	SPF_104w_M_14_2	S198
SPF	Old	Male	SPF_104w_M_2_2	S174
SPF	Old	Male	SPF_104w_M_3_2	S174
SPF	Old	Male	SPF_104w_M_4_2	S174
SPF	Old	Male	SPF_104w_M_5_2	S198
SPF	Old	Male	SPF_104w_M_6_2	S198
SPF	Old	Male	SPF_104w_M_7_2	S198
SPF	Old	Male	SPF_104w_M_8_2	S198
SPF	Old	Male	SPF_104w_M_9_2	S198
SPF	Middle-aged	Female	SPF_52w_F_1_2	S178
SPF	Middle-aged	Female	SPF_52w_F_2_2	S178
SPF	Middle-aged	Female	SPF_52w_F_3_2	S178
SPF	Middle-aged	Female	SPF_52w_F_4_2	S178
SPF	Middle-aged	Female	SPF_52w_F_5_2	S178
SPF	Middle-aged	Female	SPF_52w_F_6_2	S178
SPF	Middle-aged	Male	SPF_52w_M_1_2	S148
SPF	Middle-aged	Male	SPF_52w_M_2_2	S148
SPF	Middle-aged	Male	SPF_52w_M_3_2	S148
SPF	Middle-aged	Male	SPF_52w_M_4_2	S148
SPF	Middle-aged	Male	SPF_52w_M_5_2	S148
SPF	Young	Female	SPF_8w_F_1_2	S225
SPF	Young	Female	SPF_8w_F_3_2	S225
SPF	Young	Female	SPF_8w_F_4_2	S225
SPF	Young	Male	SPF_8w_M_2_2	S148
SPF	Young	Male	SPF_8w_M_3_2	S148
SPF	Young	Male	SPF_8w_M_4_2	S148

ME0	ME1	ME2	ME3	ME4	ME5	ME6	ME7	ME8	ME9
0.05628294	-0.15484098	0.24580761	-0.05146022	-0.03773412	0.1487267	0.001799287	0.10250437	-0.08009466	0.17194013
-0.01131610	-0.15544628	0.18694173	-0.04086877	-0.02657179	0.1285656	0.001460554	0.05570285	-0.08204890	0.03501303
-0.01916005	-0.16952548	0.22087210	-0.03977326	-0.04249419	0.2615200	-0.048604220	0.10491770	-0.02830703	0.17742336
-0.10518342	-0.14685257	0.09100536	0.04528510	-0.07699816	0.2489430	-0.109431566	0.07330849	-0.01047623	0.11103972
-0.08524261	-0.20852637	0.20740756	-0.03923631	-0.03795286	0.3546826	-0.090556839	0.06341630	-0.06267479	0.19417460
0.26432465	-0.05375806	0.11632265	-0.03499886	-0.05315761	-0.1751008	0.210900580	0.13661031	-0.04028146	-0.18311995

	Young / SPF / Male	Young / SPF / Female	Young / GF / Male	Young / GF / Female	Middle-aged / SPF / Male	Middle-aged / SPF / Female	Middle-aged / GF / Male	Middle-aged / GF / Female	Old / SPF / Male	Old / SPF / Female	Old / GF / Male	Old / GF / Female
ME0	-0.2267303	-0.33425765	-0.02070888	-0.4334783	-0.06239905	0.24833823	0.10313044	0.08745041	-0.009537167	0.25005209	0.32333113	0.01530051
ME1	0.2444567	-0.05147813	0.21252423	-0.2385979	0.33940561	-0.21373138	0.29513458	-0.37536960	0.392059078	-0.10840018	-0.33871084	-0.28447480
ME2	-0.1107945	-0.07434626	0.01312926	0.1756570	-0.24897784	0.02357208	-0.03498967	0.35254655	-0.587033526	-0.02985687	0.42451098	0.38752374
ME3	-0.2432377	0.17729366	-0.33148537	0.2024006	-0.31676621	-0.01390937	-0.34189544	0.07365013	0.564464845	-0.04514973	-0.07060893	-0.07832172
ME4	0.3959442	-0.14002887	0.40735863	-0.1778602	0.46755929	-0.15742481	0.41011754	-0.16627167	-0.407676082	-0.09449454	-0.11525209	-0.06332041
ME5	-0.1430446	0.17595879	-0.09674862	0.3796193	-0.34483042	-0.10883820	-0.30665340	0.19281946	-0.035916924	-0.10936952	0.07964452	0.31945486